Crispr/Cas9 gRNA design

Aditya M Bhagwat

Contents

1 Background

Crispr/Cas9 is a two-component genome engineering tool:

• The Cas9 enzyme performs some action at a genomic locus (cuts, activates, represses, …)
• The guide RNA spacer targets a matching locus when followed by NGG “pam” sequence

Guide RNA design involves

1. Defining target ranges
2. Transforming them according to Crispr/Cas9 application
3. Finding 20 nucleotide gRNA spacers
   • which match the target sequence (on either strand) and are followed by an NGG “pam”
   • have minimal off-target (mis)matches
   • have maximal on-target efficiency

2 Multicrispr

Multicrispr aism to be a gRNA design solution which is:

• complete: defining/transforming targets, finding spacers, analysing on/off-targets
• performant: scales towards thousands of targets (data.table backend)
• intuitive: a functional programming design and intuitive visualizations
• interoperable: acceping and returning a GRanges

For on-target scoring either the Doench2014 or the Doench2016 method can be used (Doench2016 is the current standard, see e.g. Haeussler et al., 2016). For off-target analysis Bowtie (fast) as well as vcountPDict (exact) can be used. The figure below gives an overview of how multicrispr can be used, the subsequent sections below discuss the details.

2.1 Install

Installing multicrispr is straightforward:

# From BioC
install.packages("BiocManager")
BiocManager::install(version='devel')
BiocManager::install("multicrispr")

# From gitlab: 
#url <- 'https://gitlab.gwdg.de/loosolab/software/multicrispr.git'
#remotes::install_git(url, repos = BiocManager::repositories())

Doench et al. (2016) ’s python package azimuth for on-target efficiency prediction using their method can be easily installed and activated using reticulate:

# Install once
  # reticulate::conda_create('azienv', 'python=2.7')
  # reticulate::conda_install('azienv', 'azimuth', pip = TRUE)
  # reticulate::conda_install('azienv', 'scikit-learn==0.17.1', pip = TRUE)
# Then activate
  reticulate::use_condaenv('azienv')

Bowtie-indexed genomes for fast offtarget analysis can be installed using index_genome. For the two genomes used in the examples, mm10 and hg38, the function downloads pre-build indexes from our data server, allowing a quick exploration (set download=FALSE to build index anew):

index_genome(BSgenome.Mmusculus.UCSC.mm10::BSgenome.Mmusculus.UCSC.mm10)
index_genome(BSgenome.Hsapiens.UCSC.hg38::BSgenome.Hsapiens.UCSC.hg38  )

2.2 Define targets

bed_to_granges converts a (0-based) BED coordinate file into a (1-based) GRanges.
An example is loading the 1974 binding sites of the transcription factor SRF:

require(magrittr)
require(multicrispr)
bedfile <- system.file('extdata/SRF.bed', package = 'multicrispr')
targets0 <- bed_to_granges(bedfile, genome = 'mm10')